New void fraction equations for two-phase flow in helical heat exchangers using artificial neural networks

2018 ◽  
Vol 130 ◽  
pp. 149-160 ◽  
Author(s):  
A. Parrales ◽  
D. Colorado ◽  
J.A. Díaz-Gómez ◽  
A. Huicochea ◽  
A. Álvarez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Void Fraction ◽  
Heat Exchangers ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase ◽  
Artificial Neural

ARTIFICIAL NEURAL NETWORKS IN LIQUID-LIQUID TWO-PHASE FLOW

2012 ◽  
Vol 199 (12) ◽  
pp. 1520-1542 ◽  
Author(s):  
R. Shirley ◽  
D. P. Chakrabarti ◽  
G. Das
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase ◽  
Artificial Neural

Fast classification of two-phase flow regimes based on conductivity signals and artificial neural networks

2006 ◽  
Vol 17 (6) ◽  
pp. 1511-1521 ◽  
Author(s):  
L Hernández ◽  
J E Juliá ◽  
S Chiva ◽  
S Paranjape ◽  
M Ishii
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Two Phase Flow ◽  
Flow Regimes ◽  
Phase Flow ◽  
Two Phase ◽  
Artificial Neural ◽  
Fast Classification

Two-Phase Flow Regimes in Exchangers and Piping: Part 1

2015 ◽  
Author(s):  
Siddharth Talapatra ◽  
Kevin Farrell
Keyword(s):  
Flow Visualization ◽  
Flow Regime ◽  
Void Fraction ◽  
Heat Exchangers ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase Flows ◽  
Two Phase ◽  
Gas Phases ◽  
Shell And Tube

The ability to predict the liquid-gas two-phase flow regime and void fraction in exchangers and piping is a critical engineering requirement in the process industry. The distribution of the liquid and gas phases depend on many factors including flow conditions, physical properties of the two fluids, and geometry of the flow conduit. The problem of correctly predicting the two-phase distribution is of enormous complexity, and generalized correlations that adequately describe the flow regime and/or the void fraction have not been yet been developed even for the simplest of geometries. While Computational Fluid Dynamics codes that model two-phase flows exist, they are limited in their applicability and usually require a priori knowledge of the flow regime. In this part of a two paper series, we discuss the state-of-the-art in two-phase flow regime studies inside shell-and-tube heat exchangers, while in the second part, we will discuss two-phase flows inside piping. We have performed air-water tests inside a glass shell-and-tube exchanger at HTRI, and by systematically varying various geometrical parameters, compiled the largest flow visualization database inside such exchangers. We have evaluated the best available flow regime maps available in the open literature, and shown how our results help enhance understanding of liquid-gas distribution inside heat exchangers. We have shown how, for a given flow rate, increasing the baffle spacing and reducing baffle-cut enhances two-phase separation. While these results are expected, they have never been quantified before. However, the use of flow visualization limits the liquid and gas phases to water and air mixtures, which limits the range of applicability. Shellside studies using various industrially relevant fluids such as hydrocarbon mixtures, steam water are planned, where non-visual flow regime detection techniques need to be applied.

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